{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":45867,"databundleVersionId":6924515,"sourceType":"competition"}],"dockerImageVersionId":30587,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"\n# Reading the uploaded JSON file to generate the Kaggle download commands\nimport json\n\n# Load the JSON file\nfile_path = '/kaggle/input/UBC-OCEAN/updated_image_ids.json'\nwith open(file_path, 'r') as file:\n    image_ids = json.load(file)\n\n# Generating Kaggle download commands\ncommands = [f\"kaggle competitions download UBC-OCEAN -f train_images/{image_id}.png\" for image_id in image_ids]\n\n# Joining the commands with newlines for better readability\ncommands_text = '\\n'.join(commands)\nprint(commands_text)\n\n\n","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2023-11-26T14:03:20.399734Z","iopub.execute_input":"2023-11-26T14:03:20.400178Z","iopub.status.idle":"2023-11-26T14:03:20.410723Z","shell.execute_reply.started":"2023-11-26T14:03:20.400141Z","shell.execute_reply":"2023-11-26T14:03:20.409463Z"},"trusted":true},"execution_count":null,"outputs":[]}]}